Abstract
The assessment of outcomes in medicine at an institutional and practitioner level has become target of intense scrutiny in the current practice of organized medicine. Comparison of outcomes among providers and against established benchmarks has become commonplace for the purpose of hospital accreditation and as an essential tool in the selection of providers by the consumer. Therefore, the establishment of valid metrics in the assessment of outcomes and the accurate risk-adjusted comparison of them is critical in the modern practice of medicine.
The reporting of outcomes in cardiac surgery has evolved from the release of raw mortality rates to the risk-adjusted assessment of various endpoints following an index intervention. With the advent of less invasive interventions as options for the treatment of coronary and valve pathology, there has been a significant increase in the risk profile of patients undergoing cardiac surgery and thus a wide variability in the risk profile of patients presenting to various institutions. Consequently, the establishment of accurate risk models with periodic calibration is essential to adjust for an ever-changing patient risk profile.
With the heightened scrutiny on quality measures among institutions and practitioners, it is imperative to establish effective methods of risk assessment and outcome comparison. The reporting of outcomes, initially limited to raw mortality rates, has evolved over the last three decades into the calculation of risk-adjusted metrics of a variety of quality indicators. With the intricate evolution in the complexity of organized medicine, practitioners face increasing oversight by private- and government-based regulatory entities; therefore, it is incumbent to the medical community to be knowledgeable on the various strategies in the assessment of the quality of care provided.
The current chapter intends to describe the essentials in the process of risk adjustment and to present some of the most commonly utilized registries pertinent to the practice of cardiac surgery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
https://plus.maths.org/content/florence-nightingale-compassionate-statistician.
https://www.oshpd.ca.gov/HID/Data_Request_Center/Types_of_Data.html.
https://www.resdac.org/cms-data/request/cms-data-request-center.
http://www.sts.org/news/sts-and-acc-launch-new-transcatheter-valve-therapy-registry.
http://www.sts.org/news/sts-national-database-establishes-important-link-cms-data.
Anderson RP. First publications from the society of thoracic surgeons national database. Ann Thorac Surg. 1994;57:6–7.
Austin PC. Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: a systematic review and suggestions for improvement. J Thorac Cardiovasc Surg. 2007;134:1128–35.
Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46:399–424.
Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34:3661–79.
Bewick V, Cheek L, Ball J. Statistics review 13: receiver operating characteristic curves. Crit Care. 2004;8:508–12.
Bewick V, Cheek L, Ball J. Statistics review 14: logistic regression. Crit Care. 2005;9:112–8.
Caceres M, Braud RL, Garrett HE Jr. A short history of the Society of Thoracic Surgeons national cardiac database: perceptions of a practicing surgeon. Ann Thorac Surg. 2010;89:332–9.
D’Agostino RB Jr. Propensity scores in cardiovascular research. Circulation. 2007;115:2340–3.
Deb S, Austin PC, Tu JV, Ko DT, Mazer CD, Kiss A, Fremes SE. A review of propensity-score methods and their use in cardiovascular research. Can J Cardiol. 2016;32:259–65.
Ellis H. Florence nightingale: creator of modern nursing and public health pioneer. J Perioper Pract. 2008;18:404–6.
Grover FL, Shroyer AL, Hammermeister K, Edwards FH, Ferguson TB Jr, Dziuban SW Jr, Cleveland JC Jr, Clark RE, McDonald G. A decade’s experience with quality improvement in cardiac surgery using the veterans affairs and Society of Thoracic Surgeons national databases. Ann Surg. 2001;234:464–72.
Grunkemeier GL, Jin R. Receiver operating characteristic curve analysis of clinical risk models. Ann Thorac Surg. 2001;72:323–6.
Hannan EL, Cozzens K, King SB 3rd, Walford G, Shah NR. The New York state cardiac registries: history, contributions, limitations, and lessons for future efforts to assess and publicly report healthcare outcomes. J Am Coll Cardiol. 2012;59:2309–16.
Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. New York: Wiley; 2000.
Michel P, Roques F, Nashef SA. EuroSCORE project group. Logistic or additive EuroSCORE for high-risk patients? Eur J Cardiothorac Surg. 2003;23:684–7.
Murphy M, Alavi K, Maykel J. Working with existing databases. Clin Colon Rectal Surg. 2013;26:5–11.
O'Connor GT, Plume SK, Olmstead EM, Coffin LH, Morton JR, Maloney CT, Nowicki ER, Tryzelaar JF, Hernandez F, Adrian L, et al. A regional prospective study of in-hospital mortality associated with coronary artery bypass grafting. The northern New England cardiovascular disease study group. JAMA. 1991;266:803–9.
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373–9.
Roques F, Nashef SA, Michel P, Gauducheau E, de Vincentiis C, Baudet E, Cortina J, David M, Faichney A, Gabrielle F, Gams E, Harjula A, Jones MT, Pintor PP, Salamon R, Thulin L. Risk factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19030 patients. Eur J Cardiothorac Surg. 1999;15:816–22.
Shahian DM, O’Brien SM, Filardo G, Ferraris VA, Haan CK, Rich JB, Normand S-LT, DeLong ER, Shewan CM, Dokholyan RS, Peterson ED, Edwards FH, Anderson RP. The society of thoracic surgeons 2008 cardiac surgery risk models: part 1_coronary artery bypass grafting surgery. Ann Thorac Surg. 2009;88:2–22.
Shroyer LW, Coombs LP, Peterson ED, Eiken MC, Delong ER, Chen AY, Ferguson TB, Grover FL. The Society of Thoracic Surgeons: 30-day operative mortality and morbidity risk models. Ann Thor Surg. 2003;75:1856–64.
Weintraub WS, Grau-Sepulveda MV, Weiss JM, O'Brien SM, Peterson ED, Kolm P, Zhang Z, Klein LW, Shaw RE, McKay C, Ritzenthaler LL, Popma JJ, Messenger JC, Shahian DM, Grover FL, Mayer JE, Shewan CM, Garratt KN, Moussa ID, Dangas GD, Edwards FH. Comparative effectiveness of revascularization strategies. N Engl J Med. 2012;366:1467–76.
Weiss ES, Chang DD, Joyce DL, Nwakanma LU, Yuh DD. Optimal timing of coronary artery bypass after acute myocardial infarction: a review of California discharge data. J Thorac Cardiovasc Surg. 2008;135:503–11.
Welke KF, Peterson ED, Vaughan-Sarrazin MS, O'Brien SM, Rosenthal GE, Shook GJ, Dokholyan RS, Haan CK, Ferguson TB Jr. Comparison of cardiac surgery volumes and mortality rates between the Society of Thoracic Surgeons and Medicare databases from 1993 through 2001. Ann Thorac Surg. 2007;84:1538–46.
Zou KH, O’Malley AJ, Mauri L. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation. 2007;115:654–7.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Caceres, M. (2018). Risk and Outcome Assessments. In: Dabbagh, A., Esmailian, F., Aranki, S. (eds) Postoperative Critical Care for Adult Cardiac Surgical Patients. Springer, Cham. https://doi.org/10.1007/978-3-319-75747-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-75747-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75746-9
Online ISBN: 978-3-319-75747-6
eBook Packages: MedicineMedicine (R0)